Academy PRO: Elasticsearch Misc

Oct 20, 2016Download as pptx, pdf

0 likes174 views

The document provides an overview of data aggregation methods in Elasticsearch, including metrics and bucket aggregations along with examples of curl commands for implementation. It discusses the limitations of Elasticsearch regarding SQL-like joins and outlines various use cases, including enterprise search and analytics. Additionally, it mentions client libraries available for different programming languages to interact with Elasticsearch.

Software

Elasticsearch Misc
Binary Studio Academy PRO 2016
binary-studio.com

Data aggregations
● Metrics
● Bucketing
● Pipeline (experimental)

Metrics aggregation
● avg (average value by field)
● cardinality (approximate count of distinct values)
● stats
● geo
● sum
● min
● max
● value_count

$Metrics aggregation example curl localhost:9200/github/repository/_search?pretty -d '{ "aggs" : { "stats_watchers" : { "stats" : { "field" : "watchers_count" } } } }'$

Bucket aggregation
● children
● date_range
● date_histogram
● filter(s)
● histogram
● geo
● global
● range
● ip
● terms
● nested

$Bucket aggregation histogram curl localhost:9200/github/repository/_search?pretty -d '{ "aggs" : { "prices" : { "histogram" : { "field" : "forks_count", "interval" : 1000 } } } }'$

$Bucket aggregation terms curl localhost:9200/github/repository/_search?pretty -d '{ "aggs" : { "language" : { "terms" : { "field" : "language" } } } }'$

Pipeline aggregation (experimental)
● Avg bucket
● Derivative
● Max bucket
● Min bucket
● Sum bucket
● Stats bucket
● Moving avg bucket
● Percentiles bucket

$Pipeline aggregation example curl localhost:9200/github/repository/_search?pretty -d '{ "aggs" : { "language_term" : { "terms" : { "field" : "language" } }, "language_avg":{ "stats_bucket": { "buckets_path": "language_term" } } } }'$

Relationships handling
• Application-side joins
• Data denormalization
• Nested objects
• Parent/child relationships (1:M)

Elasticsearch usages
● Enterprise search
● Analytics

Client libraries
● Java API
● elasticsearch.js
● Elasticsearch.Net and NEST
● Elasticsearch-PHP
Checkout others client libraries

var elasticsearch = require('elasticsearch');
var client = new elasticsearch.Client({
host: 'localhost:9200'
});
client.search({
index: 'github',
type: 'repository',
body: {
query: {
term: {
language: 'C++'
}
}
}
}).then(function (resp) {
var hits = resp.hits.hits;
}, function (err) {
console.trace(err.message);
});
JS
starter

$clientBuilder = ElasticsearchClientBuilder::create();
$clientBuilder->setHosts(['localhost:9200']);
$client = $clientBuilder->build();
$params = [
'index' => 'github',
'type' => 'repository',
'body' => [
'query' => [
'term' => [
'language' => 'C++'
]
]
]
];
$response = $client->search($params);
print_r($response);
PHP
starter

This document summarizes Cassandra counters and provides examples of how to use them to track real-time statistics and metrics from event data. Counters allow incrementing and decrementing column values atomically and distributing writes across the cluster. The document shows how to model event data as objects, write them to Cassandra counters bucketed by time, user, and page to enable queries for metrics like page views per minute and user usage per hour. The results demonstrate how counters prepare and organize the data for efficient reads of pre-calculated statistics.

MongoDB Analytics: Learn Aggregation by Example - Exploratory Analytics and V...MongoDB

This document discusses analyzing flight data using MongoDB aggregation. It provides examples of aggregation pipelines to group, match, project, sort, unwind and other stages. It explores questions about major carriers, airport cancellations, delays by distance and carrier. It also discusses visualizing route data and hub airports. Finally, it proposes a quiz on analyzing NYC flight data by importing data and performing queries on origins, cancellations, delays and weather impacts by month between the three major NYC airports.

Influxdb and time series dataMarcin Szepczyński

MongoDB for Time Series Data Part 2: Analyzing Time Series Data Using the Agg...MongoDB

The document discusses the implementation of a time series data aggregator using MongoDB for real-time traffic data analysis, focusing on creating a traffic dashboard supported by various data structures, including sample documents and aggregation operations. It covers topics such as document retention, historical analysis, and the use of MapReduce functions to analyze traffic impact based on weather conditions. Additionally, it highlights the benefits of a Hadoop connector for processing large datasets and improving analytic capabilities.

Script PyThoninacap

Experiment no 05Ankit Dubey

[4DEV][Łódź] Ivan Vaskevych - InfluxDB and Grafana fighting together with IoT...PROIDEA

The document discusses the collaboration between InfluxDB and Grafana in handling IoT data, presenting performance benchmarks and comparisons against PostgreSQL. It highlights the efficiency of InfluxDB with rapid data loading speeds and scalability options while providing installation instructions for AWS. Additionally, it emphasizes InfluxDB's strengths in data aggregation and high availability.

The Aggregation FrameworkMongoDB

The document discusses MongoDB's Aggregation Framework, which allows users to perform ad-hoc queries and reshape data in MongoDB. It describes the key components of the aggregation pipeline including $match, $project, $group, $sort operators. It provides examples of how to filter, reshape, and summarize document data using the aggregation framework. The document also covers usage and limitations of aggregation as well as how it can be used to enable more flexible data analysis and reporting compared to MapReduce.

Time Series Data with InfluxDBTuri, Inc.

Apache Spark - Aram MkrtchyanHovhannes Kuloghlyan

Apache Spark is a cluster computing platform designed to be fast and general-purpose. It provides a unified analytics engine for large-scale data processing across SQL, streaming, machine learning, and graph processing. Spark programs can be written in Java, Scala, Python and R. It works by building resilient distributed datasets (RDDs) that can be operated on in parallel. RDDs support transformations like map, filter and join and actions like count, collect and save. Spark also provides caching of RDDs in memory for improved performance.

Team3 presentationAmanda Gilbert

The document summarizes a Think Big Bootcamp project involving the ingestion and preliminary analysis of aircraft registry data from the FAA. It describes how the data was ingested using Python and Hadoop, then loaded into Hive tables. Initial exploration found the most frequently reported crafts and analyzed acceptance rates. Site comparison showed differences in average speed and altitude between two sites. Master data queries were created to summarize models, aircraft, and owners. Data visualizations analyzed fastest planes, speed vs altitude by make, unique flights by airline, and number of sightings by aircraft make.

"Metrics: Where and How", Vsevolod PolyakovYulia Shcherbachova

The document discusses various aspects of graphite architecture and its performance metrics, focusing on the graphite-oriented story by Vsevolod Polyakov. It covers configurations, load testing results, and performance tuning considerations for different implementations of graphite, including carbon-cache and go-carbon. The document emphasizes the importance of caching strategies and system configurations to achieve optimal request handling speeds.

Redis Day TLV 2018 - RediSearch AggregationsRedis Labs

This document discusses RediSearch aggregations, which allow processing search results to produce statistical insights. Aggregations take a search query, group and reduce the results, apply transformations, and sort. Key steps include filtering results, grouping and reducing with functions like count and average, applying expressions, and sorting. Examples show finding top GitHub committers and visits by hour. Scaling aggregations to multiple nodes requires pushing processing stages to nodes and merging results, such as summing counts or taking list intersections.

Meet the Experts: Visualize Your Time-Stamped Data Using the React-Based Gira...InfluxData

Google Cloud Storage backup and archiveIdo Green

The document covers the process of migrating, backing up, and archiving data to Google Cloud Storage (GCS), highlighting the use of tools like gsutil for data transfer and upload optimization. It discusses strategies for efficient data movement, including parallel uploads and using durable reduced availability (DRA) buckets for cost savings. Additionally, it provides practical exercises and case studies to help users understand and execute these processes effectively.

Redis Day TLV 2018 - Redis as a Time-Series DBRedis Labs

Redis can be used as a time-series database by using the redis-timeseries module. The module provides a custom data structure and commands for storing and querying time-series data in Redis. Data can be added with a timestamp and value and queried within a time range. Downsampling aggregates and stores data at regular intervals to reduce the size of long time-series data. Global configuration allows defining downsampling rules and retention policies for all keys.

Demo week three_thursPreetika Kulshrestha

The document describes MapMyCab, a tool to analyze cab occupancy, miles travelled, pickups and drop-offs by time of day or day of week using real-time streaming data from cabs. It collects GPS data from hundreds of cabs including location, occupancy and timestamp. This data is streamed to HBase and MapReduce jobs are run to generate hourly and daily aggregates which are stored back in HBase for fast retrieval.

2016 foss4 g track: developing and implementing spatial etl processes with...GIS in the Rockies

This document discusses developing and implementing spatial ETL processes using open source tools like PostGIS, Python, and cron jobs. Key points include replacing ArcSDE with PostGIS for development and QA, building Python ETL scripts to extract data from SQL Server and PostGIS, transform it using PostGIS functions, and load it into PostGIS and SQL Server, and deploying the ETL processes on an Ubuntu server with cron jobs to run daily. The goal is to break dependencies on proprietary tools and build fully open source and automated spatial ETL.

Optimizing joins in Map reduce jobs via Lookup ServiceRohit kochar

This document discusses optimizing joins in MapReduce jobs by using a dimension store lookup service. The initial approach of performing joins during the map phase led to increased memory usage and job setup times. To address this, a dimension store was implemented using Aerospike to provide high throughput lookups of small dimension tables to optimize joins. This reduced total job time by 30-40% and improved the performance of real-time ETL jobs that involved multiple joins.

Quick 入門 | iOS RDD テストフレームワーク for Swift/Objective-CYuki Tanabe

I See NoSQL Document Stores in Geospatial ApplicationsIBM Cloud Data Services

The document discusses IBM Cloud Data Services and the use of NoSQL document stores, particularly Cloudant, for handling geospatial data. It covers features like spatial queries, bulk document upload, and high-performance geospatial databases, as well as various operations performed on multiple databases. Additionally, it mentions integration with tools like Koop and ArcGIS for managing and querying geospatial data.

Kapacitor Alert Topic handlersInfluxData

Kissy component systemyiming he

The document discusses KISSY Component System, an infrastructure for building reusable UI components. It describes key concepts like classes, attribute management, base classes, and life cycle methods. It also provides examples of common UI components like draggable, resizable, and controls. Custom components can be built by extending base classes and implementing render and logic code. The KISSY framework supports building complex components and applications with components, containers, and events.

Daniel Sikar: Hadoop MapReduce - 06/09/2010 Skills Matter

This document discusses using Hadoop MapReduce on Amazon Web Services for parallel processing of large datasets in a distributed manner. It provides an example of counting word frequencies from log files using Hadoop Streaming to run MapReduce jobs with scripts. The steps shown include launching an Elastic MapReduce cluster, uploading data and scripts to S3, running a MapReduce job to count word frequencies from Wikipedia log files, and then terminating the cluster.

Declarative Infrastructure Tools Yulia Shcherbachova

The document, presented by Alex Conway, discusses various declarative infrastructure tools, focusing on automation and simplified interfaces for managing complex environments. It highlights tools such as Terraform for infrastructure as code, Docker Compose for multi-container applications, and custom solutions like 'ltparse' for generating Terraform configurations with improved readability. Overall, the emphasis is on empowering developers to efficiently create and manage independent test environments using declarative methodologies.

Bubble in link listuniversity of Gujrat, pakistan

This document provides code to sort a linked list in ascending order using bubble sort. It defines a Node class with data and pointer properties. It then prompts the user to enter nodes, stores them in a linked list, and displays the unsorted list. The code implements bubble sort to iterate through the list, compare adjacent nodes, and swap values if out of order. Finally, it displays the sorted linked list.

Google App Engine BeCamp 2008atonse

The Google App Engine is a sandboxed Python environment designed for developing applications, including a demo calculator app by Ashish Tones and Prasad Silva. It utilizes Google's Bigtable database with specific restrictions such as no access to local filesystems or raw sockets. Data storage and management are simplified using a modified Python model framework with properties for managing variables and user ownership.

Introduction to elasticsearchFlorian Hopf

Elasticsearch is a Java-based full-text search server featuring document-based storage, HTTP communication, and JSON support, with indexing and search capabilities using query DSL. It also supports scaling through nodes forming clusters, shard distribution, and replica management for fault tolerance. The document outlines features like log centralization via tools like Logstash, ingestion, and real-time analysis with visualization through Kibana.

Elasticsearch first-stepsMatteo Moci

This document discusses using Elasticsearch for social media analytics and provides examples of common tasks. It introduces Elasticsearch basics like installation, indexing documents, and searching. It also covers more advanced topics like mapping types, facets for aggregations, analyzers, nested and parent/child relations between documents. The document concludes with recommendations on data design, suggesting indexing strategies for different use cases like per user, single index, or partitioning by time range.

Real-time Data Analytics mit Elasticsearchinovex GmbH

The document provides an overview of real-time data analytics using Elasticsearch, covering its architecture, data ingestion methods, analysis capabilities, and visualization tools. It discusses key components such as Logstash for data collection and management, as well as the integration of Elasticsearch with Hadoop for enhanced analytics. Additionally, it highlights the use of aggregations for data analysis and the visualization capabilities through Kibana.

More Related Content

What's hot (19)

Time Series Data with InfluxDBTuri, Inc.

Apache Spark - Aram MkrtchyanHovhannes Kuloghlyan

Team3 presentationAmanda Gilbert

"Metrics: Where and How", Vsevolod PolyakovYulia Shcherbachova

Redis Day TLV 2018 - RediSearch AggregationsRedis Labs

Meet the Experts: Visualize Your Time-Stamped Data Using the React-Based Gira...InfluxData

Google Cloud Storage backup and archiveIdo Green

Redis Day TLV 2018 - Redis as a Time-Series DBRedis Labs

Demo week three_thursPreetika Kulshrestha

2016 foss4 g track: developing and implementing spatial etl processes with...GIS in the Rockies

Optimizing joins in Map reduce jobs via Lookup ServiceRohit kochar

Quick 入門 | iOS RDD テストフレームワーク for Swift/Objective-CYuki Tanabe

I See NoSQL Document Stores in Geospatial ApplicationsIBM Cloud Data Services

Kapacitor Alert Topic handlersInfluxData

Kissy component systemyiming he

Daniel Sikar: Hadoop MapReduce - 06/09/2010 Skills Matter

Declarative Infrastructure Tools Yulia Shcherbachova

Bubble in link listuniversity of Gujrat, pakistan

Google App Engine BeCamp 2008atonse

Time Series Data with InfluxDBTuri, Inc.

Apache Spark - Aram MkrtchyanHovhannes Kuloghlyan

Team3 presentationAmanda Gilbert

"Metrics: Where and How", Vsevolod PolyakovYulia Shcherbachova

Redis Day TLV 2018 - RediSearch AggregationsRedis Labs

Meet the Experts: Visualize Your Time-Stamped Data Using the React-Based Gira...InfluxData

Google Cloud Storage backup and archiveIdo Green

Redis Day TLV 2018 - Redis as a Time-Series DBRedis Labs

Demo week three_thursPreetika Kulshrestha

2016 foss4 g track: developing and implementing spatial etl processes with...GIS in the Rockies

Optimizing joins in Map reduce jobs via Lookup ServiceRohit kochar

Quick 入門 | iOS RDD テストフレームワーク for Swift/Objective-CYuki Tanabe

I See NoSQL Document Stores in Geospatial ApplicationsIBM Cloud Data Services

Kapacitor Alert Topic handlersInfluxData

Kissy component systemyiming he

Daniel Sikar: Hadoop MapReduce - 06/09/2010 Skills Matter

Declarative Infrastructure Tools Yulia Shcherbachova

Bubble in link listuniversity of Gujrat, pakistan

Google App Engine BeCamp 2008atonse

Similar to Academy PRO: Elasticsearch Misc (20)

Introduction to elasticsearchFlorian Hopf

Elasticsearch first-stepsMatteo Moci

Real-time Data Analytics mit Elasticsearchinovex GmbH

ElasticsearchAndrii Gakhov

Elasticsearch 1.1.0 includes several new features and improvements such as new aggregation types like cardinality and percentiles, significant terms aggregation, and improvements to terms and multi-field search. It also includes breaking changes to configuration, multi-fields, stopwords, and return values. New features for aggregations include bucketing and metrics aggregations as well as the ability to add sub-aggregations. Backup and restore capabilities were added through repositories and snapshots. The tribe feature allows federation across multiple clusters.

Elasticsearch: Getting Started Part 3 AggregationsSuyog Kale

The document provides an overview of Elasticsearch aggregations, explaining their purpose in generating aggregated data from search queries. It details metrics aggregations such as average, cardinality, extended stats, min, max, and sum, as well as bucket aggregations and nested bucket aggregations for analyzing complex data patterns. This functionality is particularly useful for organizations needing to analyze large datasets, like e-commerce user behavior by location or demographics.

Elasticsearch for Data AnalyticsFelipe

This document provides an overview of using Elasticsearch for data analytics. It discusses various aggregation techniques in Elasticsearch like terms, min/max/avg/sum, cardinality, histogram, date_histogram, and nested aggregations. It also covers mappings, dynamic templates, and general tips for working with aggregations. The main takeaways are that aggregations in Elasticsearch provide insights into data distributions and relationships similarly to GROUP BY in SQL, and that mappings and templates can optimize how data is indexed for aggregation purposes.

Elasticsearch Introduction at BigData meetupEric Rodriguez (Hiring in Lex)

The document provides an introduction to Elasticsearch, a distributed search and analytics engine built on Apache Lucene. It outlines its architecture, core features, key components including Logstash and Kibana, and various use cases such as full-text search and data storage. Additionally, it discusses Elasticsearch's capabilities in handling high availability through sharding and replication, along with its extensive ecosystem of integrations and clients.

Using elasticsearch with railsTom Z Zeng

Elasticsearch is a powerful open source search and analytics engine. It allows for full text search capabilities as well as powerful analytics functions. Elasticsearch can be used as both a search engine and as a NoSQL data store. It is easy to set up, use, scale, and maintain. The document provides examples of using Elasticsearch with Rails applications and discusses advanced features such as fuzzy search, autocomplete, and geospatial search.

Big data elasticsearch practicalJWORKS powered by Ordina

This document provides an overview and instructions for setting up Elasticsearch. It discusses: - How to set up the Elasticsearch workshop environment by installing required software and cloning the GitHub repository. - Key concepts about Elasticsearch including its distributed and schema-free nature, how it is document oriented, and how indexes, types, documents, and fields relate to a relational database. - Core components like clusters, nodes, shards, and replicas. It also distinguishes between filters and queries. - Steps for connecting to Elasticsearch, inserting, searching, updating and deleting data. - Advanced search techniques including filters, multi-field search, and human language processing using analyzers, stop words, synonyms and normalization

ElasticSearch: Distributed Multitenant NoSQL Datastore and Search EngineDaniel N

The document provides an overview of a distributed multitenant NoSQL datastore and search engine, emphasizing concepts such as SQL vs NoSQL, the architecture of Elasticsearch, and core components like indices, shards, and nodes. It highlights the capabilities of Elasticsearch, including cloud compatibility, RESTful API support, and its scalability options. Additionally, it discusses practical usage examples, typical queries, and integrations with AWS services.

Making your elastic cluster perform - Jettro Coenradie - Codemotion Amsterdam...Codemotion

The document provides a comprehensive guide on optimizing performance for an Elasticsearch cluster, covering installation, configuration, indexing, and querying strategies. It highlights key considerations such as hardware selection, index design, and mapping while offering practical command examples for configuration and data management. Additionally, it emphasizes monitoring tools and logging settings to enhance cluster visibility and performance troubleshooting.

MongoDB's New Aggregation frameworkChris Westin

The document discusses MongoDB's new aggregation framework, which provides a declarative pipeline for performing data aggregation operations on complex documents. The framework allows users to describe a chain of operations without writing JavaScript. It will offer high-performance operators like $match, $project, $unwind, $group, $sort, and computed expressions to reshape and analyze document data without the overhead of JavaScript. The aggregation framework is nearing release and will support sharding by forwarding pipeline operations to shards and combining results.

Elasto Maniaandrefsantos

Elasticsearch is an open source, distributed, real-time search and analytics engine. It allows users to store and search large volumes of data in near real-time. The presentation provided an overview of Elasticsearch, including its data model of documents, indexes, and types; how to perform CRUD operations; searching functionality; and common tools that integrate with Elasticsearch like Logstash and Kibana. Advanced features like aggregations, percolations, and scaling were also briefly discussed.

mongodb-aggregation-may-2012Chris Westin

The document outlines a new aggregation framework that simplifies data processing by reducing reliance on JavaScript, allowing for a series of operations called pipelines to efficiently process complex documents. Key operations include $match, $project, $unwind, $group, $sort, and various computed expressions for data manipulation. Plans for optimization, sharding support, and future enhancements are also discussed, with the framework currently in final development and expected to be released soon.

Elasticsearch & "PeopleSearch"George Stathis

The document discusses the use of Elasticsearch at Traackr for building a people discovery engine, migrating from Solr in 2012. It covers Elasticsearch's features such as near real-time search, automatic schema detection, and document types, while also addressing search performance challenges and alternatives like Solr grouping and denormalization. The presentation includes examples of search queries, schema mapping, and the advantages of using Elasticsearch over other solutions for real-time search needs.

Elasticsearch - basics and beyondErnesto Reig

Elasticsearch is a distributed, scalable, and highly available search and analytics engine. The key points discussed in the document include proper cluster configuration, data mappings, and monitoring. The document outlines Elasticsearch's architecture including shards, replicas, and cluster topology. It also discusses modeling data through mappings, analysis, and handling relationships. Monitoring is important and can be done through Elasticsearch plugins, JVM tools, and the stats API.

Simple search with elastic searchmarkstory

Elasticsearch is a JSON document database that allows for powerful full-text search capabilities. It uses Lucene under the hood for indexing and search. Documents are stored in indexes and types which are analogous to tables in a relational database. Documents can be created, read, updated, and deleted via a RESTful API. Searches can be performed across multiple indexes and types. Elasticsearch offers advanced search features like facets, highlighting, and custom analyzers. Mappings allow for customization of how documents are indexed. Shards and replicas improve performance and availability. Multi-tenancy can be achieved through separate indexes or filters.

Elasticsearch Tutorial | Getting Started with Elasticsearch | ELK Stack Train...Edureka!

The document provides an overview of Elasticsearch, describing it as a real-time, distributed analytics engine based on Lucene, with various functionalities including full-text search and scalability. It outlines the advantages of Elasticsearch, installation steps, core concepts like clusters and indices, and discusses API conventions and types. Lastly, it details CRUD operations and query DSL for effective data management and analysis.

Elastc SearchSivanagaraju Pachipulusu

ELK - What's new and showcasesAndrii Gakhov

The document discusses recent updates and features of Elasticsearch and Kibana, highlighting the open-source search engine's scalable design, enhanced security with Shield, and new visualization capabilities. Key features include user authentication, advanced aggregation methods, a new user interface, and SSL encryption for secure communications. It also presents various aggregation examples and solutions for analyzing data, focusing on performance improvements and new analytic capabilities since version 1.2.0.

Introduction to elasticsearchFlorian Hopf

Elasticsearch first-stepsMatteo Moci

Real-time Data Analytics mit Elasticsearchinovex GmbH

ElasticsearchAndrii Gakhov

Elasticsearch: Getting Started Part 3 AggregationsSuyog Kale

Elasticsearch for Data AnalyticsFelipe

Elasticsearch Introduction at BigData meetupEric Rodriguez (Hiring in Lex)

Using elasticsearch with railsTom Z Zeng

Big data elasticsearch practicalJWORKS powered by Ordina

ElasticSearch: Distributed Multitenant NoSQL Datastore and Search EngineDaniel N

Making your elastic cluster perform - Jettro Coenradie - Codemotion Amsterdam...Codemotion

MongoDB's New Aggregation frameworkChris Westin

Elasto Maniaandrefsantos

mongodb-aggregation-may-2012Chris Westin

Elasticsearch & "PeopleSearch"George Stathis

Elasticsearch - basics and beyondErnesto Reig

Simple search with elastic searchmarkstory

Elasticsearch Tutorial | Getting Started with Elasticsearch | ELK Stack Train...Edureka!

Elastc SearchSivanagaraju Pachipulusu

ELK - What's new and showcasesAndrii Gakhov

More from Binary Studio (20)

Academy PRO: D3, part 3Binary Studio

This document discusses using HTML5 Canvas with D3.js for data visualization. It provides an overview of Canvas, noting that it is lower level but more performant than SVG for drawing many objects on a small surface like data visualizations. It then lists 3 ways D3.js can be used with Canvas, including using D3.js for data transformation and repainting Canvas on data changes or animations. The document provides examples of drawing, data binding, axes, transitions, interactivity, and drag and drop on Canvas using D3.js and links to additional Canvas tutorials.

Academy PRO: D3, part 1Binary Studio

Data visualization is the presentation of data in a graphical format. Visuals are processed faster by the brain and help reveal patterns, trends, and correlations in the data. D3 is a popular JavaScript library for visualizing data using web standards like SVG, Canvas, and HTML. It allows powerful data-driven transformations of documents by binding data to elements. Scales map abstract data dimensions to visual representations.

Academy PRO: Cryptography 3Binary Studio

The document discusses various cryptographic concepts such as MACs, hashes, key derivation functions, and authenticated encryption. It describes techniques for constructing secure MACs like ECBC-MAC and PMAC, and explains attacks like length-extension attacks and collision attacks. The document also covers standards for authenticated encryption like GCM, CCM, and EAX that combine encryption and message authentication codes.

Academy PRO: Cryptography 1Binary Studio

Cryptography is used in many applications including data encryption, digital communications, digital signatures, and content protection. It relies on principles of number theory and concepts like primes and one-way functions. The one-time pad provides perfect secrecy but requires a truly random key of the same length as the message. Stream ciphers expand a small key into a pseudorandom stream using a pseudorandom generator, allowing encryption and decryption at high speeds though perfect secrecy is not achieved. Semantic security provides protection against chosen plaintext attacks by ensuring an adversary cannot determine which of two messages was encrypted.

Academy PRO: Advanced React Ecosystem. MobXBinary Studio

MobX is a library that uses functional reactive programming to simplify state management in applications. It uses decorators like @observable, @computed, and @action to transparently observe state changes and automatically update dependent views. MobX ensures state and views remain consistent by deriving all values and automatically running reactions when the source data changes. MobX also includes tools for debugging observable state and integrating with React applications.

Academy PRO: Docker. Part 4Binary Studio

The document discusses Docker and container orchestration tools. It begins with an agenda on multi-machine Docker swarms and alternatives like Kubernetes and Mesos. It then covers setting up a multi-node Docker swarm across two virtual machines, deploying an application to the swarm, and accessing the clustered application. Moby Project is introduced as the new name for Docker's open source components to distinguish them from commercial Docker products. Tools like Kitematic, Docker's Universal Control Plane, and Panamax are also briefly mentioned.

Academy PRO: Docker. Part 2Binary Studio

The document provides an overview of containerization basics using Docker. It defines key Docker terminology like images, containers, daemon, client, and Docker Hub. It explains how to run a static website in a container, view running containers and images, build and push custom images to a private registry. It also covers container logging and setting up a private Docker registry using the registry image.

Academy PRO: Docker. Part 1Binary Studio

This document provides an agenda for a Docker Academy PRO course. It introduces containers and containerization basics. It discusses how Docker works and the evolution of IT that led to its development. It compares containers to virtual machines and the advantages of containers. Key Docker concepts are explained like images, the Docker daemon, and official Docker images. The document concludes by asking if there are any questions.

Binary Studio Academy 2017: JS team project - OrderlyBinary Studio

The document discusses a technology developed by the Binary Studio Academy team, which combines the ease of spreadsheets with the capabilities of a database for organizing various tasks. It features powerful filtering and sorting, collaboration tools, and the ability to share forms that feed responses directly into the database. Users can also access information through a mobile app with tap-friendly card presentations.

Binary Studio Academy 2017: .NET team project - UnicornBinary Studio

Academy PRO: React native - miscellaneousBinary Studio

This document discusses various topics in React Native including animations, notifications, storage, and push notifications. It provides code examples for using pan responders and gestures to handle touches, creating websockets using Socket.io or native code, different types of animations like timing and spring, and accessing async storage on Android and iOS. It also covers using AppState to detect app state changes and sending local and scheduled push notifications.

Academy PRO: React native - publishBinary Studio

This document provides an overview of React Native including building initial scenes, navigation, animations, performance optimization, building APKs, and publishing to app stores. It discusses key aspects like the main UI thread frame rate, tools for profiling performance, avoiding performance killers like console logs, and using the Animated API. It also covers building a release key, generating a signed APK, and integrating third-party libraries like Expo and CodePush.

Academy PRO: React native - navigationBinary Studio

This document provides an overview of navigation options and best practices for React Native applications. It discusses the Navigator API, common navigation libraries like React Navigation and React Native Router Flux, and how to structure navigation within a React/Redux project using components, actions, and reducers. It also covers animations, drawer navigation, and common caveats to consider with lists and preserving internal state.

Academy PRO: React native - building first scenesBinary Studio

This document provides an overview of building initial scenes in React Native, including setting up components, styles, state, props, file structure, and networking. It discusses common React Native components like View, Text, Image, ScrollView, ListView, and interaction components. It also covers more advanced topics such as maps, dates pickers, geolocation, cameras, and working with native iOS and Android modules.

Academy PRO: React Native - introductionBinary Studio

The document provides an overview of React Native, including its history and timeline, components, styles, performance, debugging tools, and how to set up a React Native application. Some key points covered include: - React Native allows building native mobile apps using JavaScript and React by rendering UI components to native platform views. - It uses the same fundamental UI building blocks as regular iOS and Android apps like Views, Text, Image, ScrollView etc. but they are implemented using JavaScript and React. - Styles are defined using JavaScript objects with the StyleSheet.create method, allowing flexible styling similar to CSS but optimized for mobile. - Under the hood, React Native combines the fundamental native platform APIs with

Academy PRO: Push notifications. Denis BeketskyBinary Studio

Academy PRO: Docker. Lecture 4Binary Studio

The document discusses Docker and container orchestration tools. It begins with an agenda on multi-machine Docker swarms and alternatives like Kubernetes and Mesos. It then provides step-by-step instructions for setting up a multi-node Docker swarm cluster on VirtualBox machines and deploying an application. The document also discusses the Moby Project for separating Docker's open source and commercial components, as well as other Docker tools for developers.

Academy PRO: Docker. Lecture 3Binary Studio

This document provides a summary of key concepts in Docker orchestration and networking: - It describes the default Docker networks (bridge, none, host) and how to create custom networks for containers to communicate. - Docker Compose is introduced as a tool to define and run multi-container applications using a compose file to configure services. - Docker Swarm mode allows deploying containers across multiple Docker hosts as a cluster, with services that can be scaled out and updated on the swarm. Managers and workers are node types in a swarm. - Key features for swarm deployments using Docker Compose files are discussed, including resources, update configurations, and restart policies.

Academy PRO: Docker. Lecture 2Binary Studio

Academy PRO: Docker. Lecture 1Binary Studio

This document provides an agenda for a Docker Academy PRO course. It introduces Docker and containerization basics, including what containers are, how they work, and the challenges they solve compared to traditional virtual machines. It discusses Docker specifically, how it helps build and deploy applications, and how the Docker ecosystem works with components like Containerd and runC. Overall it serves as an introduction and overview to Docker and containerization concepts.

Academy PRO: D3, part 3Binary Studio

Academy PRO: D3, part 1Binary Studio

Academy PRO: Cryptography 3Binary Studio

Academy PRO: Cryptography 1Binary Studio

Academy PRO: Advanced React Ecosystem. MobXBinary Studio

Academy PRO: Docker. Part 4Binary Studio

Academy PRO: Docker. Part 2Binary Studio

Academy PRO: Docker. Part 1Binary Studio

Binary Studio Academy 2017: JS team project - OrderlyBinary Studio

Binary Studio Academy 2017: .NET team project - UnicornBinary Studio

Academy PRO: React native - miscellaneousBinary Studio

Academy PRO: React native - publishBinary Studio

Academy PRO: React native - navigationBinary Studio

Academy PRO: React native - building first scenesBinary Studio

Academy PRO: React Native - introductionBinary Studio

Academy PRO: Push notifications. Denis BeketskyBinary Studio

Academy PRO: Docker. Lecture 4Binary Studio

Academy PRO: Docker. Lecture 3Binary Studio

Academy PRO: Docker. Lecture 2Binary Studio

Academy PRO: Docker. Lecture 1Binary Studio

Recently uploaded (20)

From Data Preparation to Inference: How Alluxio Speeds Up AIAlluxio, Inc.

Alluxio Webinar June 17, 2025 For more Alluxio Events: https://www.alluxio.io/events/ Speaker: Jingwen Ouyang (Sr. Product Manager @ Alluxio) In this talk, Jingwen Ouyang, Senior Product Manager at Alluxio, will share how Alluxio make it easy to share and manage data from any storage to any compute engine in any environment with high performance and low cost for your model training, model inference, and model distribution workload.

Why Every Growing Business Needs a Staff Augmentation Company IN USA.pdfmary rojas

U.S. staff augmentation companies excel at providing IT professionals with expertise in development, cybersecurity, AI, and more. Whether you need to fill a short-term gap or enhance your team’s capabilities long-term, these firms deliver pre-vetted talent tailored to your tech stack and industry. With an understanding of both global technologies and local compliance, U.S.-based IT staff augmentation bridges the gap between talent shortage and project demand efficiently.

declaration of Variables and constants.pptxmeemee7378

IDM Crack with Internet Download Manager 6.42 Build 41 [Latest 2025]pcprocore

👉Copy link &paste Google new tab > https://pcprocore.com/ 👈✅ IDM Crack with Internet Download Manager (IDM) is a tool to increase download speeds, resume, and schedule downloads.Comprehensive error recovery and resume capability will restart broken or interrupted downloads due to lost connections, network problems, computer shutdowns, or unexpected power outages. The simple graphic user interface makes IDM user-friendly and easy to use. Internet Download Manager has a smart download logic accelerator with intelligent dynamic file segmentation and safe multipart downloading technology to accelerate your downloads. Unlike other download managers and accelerators, Internet Download Manager segments downloaded files dynamically during the download process and reuses available connections without additional connect and login stages to achieve the best acceleration performance for IDM users. System Requirements of IDM: Operating System: Windows XP, NT, 2000, Vista, 7, 8, 8.1, 10 & 11 (32bit and 64bit) Memory (RAM): 512 MB of RAM required Hard Disk Space: 25 MB of free space required for full installation Processor: Intel Pentium 4 Dual Core GHz or higher. Ensure your system meets these requirements for the IDM Crack to function properly. How to Install and Crack: Temporarily disable the antivirus until the IDM Crack process Install “idman642build41.exe” Extract “IDM 6.42.41 Patch.rar” (Password is: 123) Install “IDM 6.42.41 Patch.exe” Done!!! Enjoy!!!

CodeCleaner: Mitigating Data Contamination for LLM Benchmarkingarabelatso

Threat Modeling a Batch Job Framework - Teri Radichel - AWS re:Inforce 20252nd Sight Lab

This presentation is similar to another presentation I gave earlier in the year at the AWS Community Security Day, except that I had less time to present and incorporated some AI slides into the presentation. In this presentation I explain what batch jobs are, how to use them, and that AI Agents are essentially batch jobs. All the same security controls that apply to batch jobs also apply to AI agents. In addition, we have more concerns with AI agents because AI is based on a statistical model. That means that depending on where and how AI is used we lose some of the reliability that we would get from a traditional batch job. That needs to be taken into consideration when selecting how and when to use AI Agents as batch jobs and whether we should use AI to trigger an Agent. As I mention on one side - I don't want you to predict what my bank statement should look like. I want it to be right! By looking at various data breaches we can determine how those attacks worked and whether the system we are building is susceptible to a similar attack or not.

Folding Cheat Sheet # 9 - List Unfolding 𝑢𝑛𝑓𝑜𝑙𝑑 as the Computational Dual of ...Philip Schwarz

Why Edge Computing Matters in Mobile Application Tech.pdfIMG Global Infotech

Best Practice for LLM Serving in the CloudAlluxio, Inc.

Alluxio Webinar June 17, 2025 For more Alluxio Events: https://www.alluxio.io/events/ Speaker: Nilesh Agarwal (Co-founder & CTO @ Inferless) Nilesh Agarwal, co-founder & CTO at Inferless, shares insights on accelerating LLM inference in the cloud using Alluxio, tackling key bottlenecks like slow model weight loading from S3 and lengthy container startup time. Inferless uses Alluxio as a three-tier cache system that dramatically cuts model load time by 10x.

From Code to Commerce, a Backend Java Developer's Galactic Journey into Ecomm...Jamie Coleman

In a galaxy not so far away, a Java developer advocate embarks on an epic quest into the vast universe of e-commerce. Armed with backend languages and the wisdom of microservice architectures, set out with us to learn the ways of the Force. Navigate the asteroid fields of available tools and platforms; tackle the challenge of integrating location-based technologies into open-source projects; placate the Sith Lords by enabling great customer experiences. Follow me on this journey from humble Java coder to digital marketplace expert. Through tales of triumph and tribulation, gain valuable insights into conquering the e-commerce frontier - such as the different open-source solutions available - and learn how technology can bring balance to the business Force, large or small. May the code be with you.

Test Case Design Techniques – Practical Examples & Best Practices in Software...Muhammad Fahad Bashir

This presentation was part of the SQA & PM Bootcamp, where I served as a trainer. It focuses on effective test case design techniques, blending theoretical knowledge with practical application, especially useful for manual testers and QA beginners. Video Lecture Recording : https://www.facebook.com/share/v/1Z44DiXN5v/ What’s Covered: 🧾 Definition and Purpose of Test Case Design 🚀 Importance of Structured Test Design 🧠 Test Case Design Techniques Overview: ✅ Black Box Techniques ✅ White Box Techniques ✅ Experience-Based Techniques 📝 Manual Test Case Design in Detail 🔐 Real-World Example: Login Page Test Case 🧩 Black Box Methods Explained with Examples: Equivalence Partitioning (EP) Boundary Value Analysis (BVA) Difference Between EP & BVA 📊 Decision Table Testing 💡 Experience-Based Testing: Error Guessing, Exploratory Testing 📂 White Box Techniques (Introductory Overview) ➕ Much More to Help You Build Strong Manual Testing Skills This resource is especially helpful for students, aspiring QA professionals, and junior testers looking to master test design fundamentals with clarity and practical insight.

Simplify Insurance Regulations with Compliance Management SoftwareInsurance Tech Services

Streamlining CI/CD with FME Flow: A Practical GuideSafe Software

Advance Doctor Appointment Booking App With Online PaymentAxisTechnolabs

Y - Recursion The Hard Way GopherCon EU 2025Eleanor McHugh

In most modern programming languages, including Go, it's possible for a function to call itself recursively. It's such a mainstream technique that we've come to take it for granted. But what if functions couldn't call themselves? There's a branch of mathematics called Combinatorics which answers just these kinds of questions, and this presentation takes these ideas and phrases them in Go to teach a deeper view of computation through the medium of code.

Sysinfo OST to PST Converter InfographicSysInfo Tools

Humans vs AI Call Agents - Qcall.ai's Special ReportUdit Goenka

Humans vs Agentic AI Cost Comparison: Cut 85% Support Costs TL;DR: Agentic AI crushes human costs in L1/L2 support with 85% savings, 300% efficiency gains, and 90%+ success rates. Your CFO just asked: "Why spend $2.3M annually on L1 support when competitors automated 80% for under $200K?" The humans vs agentic AI cost debate isn't theoretical - it's happening now. Real Cost Breakdown: - India L1/L2 Agent: ₹6,64,379 ($8,000) annually (full cost) - US L1/L2 Agent: $70,000 annually (loaded cost) - Qcall.ai Agentic AI: ₹6/min ($0.07/min) = 85% savings Game-Changing Results: - 47 human agents → 8 agents + AI system - Operating costs: $2.8M → $420K (85% reduction) - Customer satisfaction: 78% → 91% - First call resolution: 72% → 94% - Languages: 2-3 → 15+ fluently Why Agentic AI Wins: - Human Limitations: 8hrs/day, 1-2 languages, 30-45% attrition, mood-dependent, 6-12 weeks training - Qcall.ai Advantages: 24/7 availability, 97% humanized voice, <60 second response, intelligent qualification, seamless handoff Industry Impact: - E-commerce: 82% cost reduction - Healthcare: 90% appointment automation - SaaS: 23% satisfaction improvement - Financial: 95% inquiry automation Implementation: - Setup: 2-5 days vs 6-12 weeks hiring - ROI: Break-even in 3 months - Scaling: Instant global expansion Competitive Gap: - Early Adopters: 85% cost advantage, superior metrics, faster expansion Late Adopters: Higher costs, limited scalability, compounding disadvantage Similar automation patterns emerge across industries. Marketing teams use autoposting.ai for social media automation - same human-AI collaboration model driving L1/L2 transformation. "We reduced support costs by 78% while improving satisfaction by 31%. ROI was immediate and keeps compounding." - COO, Global SaaS Company Getting Started: Calculate current L1/L2 costs Assess repetitive tasks Request Qcall.ai demo Plan 20% pilot Bottom Line: Companies avoiding this shift hemorrhage millions while competitors scale globally with fraction of costs. The question isn't whether agentic AI will replace routine tasks - it's whether you'll implement before competitors gain insurmountable advantage. Ready to transform operations? This reveals complete cost analysis, implementation strategy, and competitive advantages.

Top Time Tracking Solutions for Accountantsoliviareed320

This presentation explores the importance of time tracking for accountants and introduces modern solutions that help streamline billable hours, improve productivity, and eliminate manual tracking errors. Learn how time tracking software tailored for accounting professionals can simplify client billing, ensure compliance, and enhance profitability. From key challenges in time management to top features to look for in a solution, this presentation offers valuable insights and practical tips for solo accountants, finance teams, and accounting firms looking to optimize their time-tracking workflows.https://www.invoicera.com/blog/invoicing/best-time-tracking-software-for-accountants/

IObit Driver Booster Pro 12 Crack Latest Version Downloadpcprocore

ERP Systems in the UAE: Driving Business Transformation with Smart Solutionsdheeodoo

ERP solutions are driving innovative change for businesses across the UAE through improved efficiency, reduced operational costs, and provide real-time visibility of operational data. By offering native Arabic language support, VAT compliance, and some level of cloud readiness, ERP solutions also supports and are consistent with the initiative for Smart Dubai 2030. Odoo is an example of a flexible & scalable solution. If you're looking for expert support with implementation contact Banibro. www.banibro.com

From Data Preparation to Inference: How Alluxio Speeds Up AIAlluxio, Inc.

Why Every Growing Business Needs a Staff Augmentation Company IN USA.pdfmary rojas

declaration of Variables and constants.pptxmeemee7378

IDM Crack with Internet Download Manager 6.42 Build 41 [Latest 2025]pcprocore

CodeCleaner: Mitigating Data Contamination for LLM Benchmarkingarabelatso

Threat Modeling a Batch Job Framework - Teri Radichel - AWS re:Inforce 20252nd Sight Lab

Folding Cheat Sheet # 9 - List Unfolding 𝑢𝑛𝑓𝑜𝑙𝑑 as the Computational Dual of ...Philip Schwarz

Why Edge Computing Matters in Mobile Application Tech.pdfIMG Global Infotech

Best Practice for LLM Serving in the CloudAlluxio, Inc.

From Code to Commerce, a Backend Java Developer's Galactic Journey into Ecomm...Jamie Coleman

Test Case Design Techniques – Practical Examples & Best Practices in Software...Muhammad Fahad Bashir

Simplify Insurance Regulations with Compliance Management SoftwareInsurance Tech Services

Streamlining CI/CD with FME Flow: A Practical GuideSafe Software

Advance Doctor Appointment Booking App With Online PaymentAxisTechnolabs

Y - Recursion The Hard Way GopherCon EU 2025Eleanor McHugh

Sysinfo OST to PST Converter InfographicSysInfo Tools

Humans vs AI Call Agents - Qcall.ai's Special ReportUdit Goenka

Top Time Tracking Solutions for Accountantsoliviareed320

IObit Driver Booster Pro 12 Crack Latest Version Downloadpcprocore

ERP Systems in the UAE: Driving Business Transformation with Smart Solutionsdheeodoo

Academy PRO: Elasticsearch Misc

1. Elasticsearch Misc Binary Studio Academy PRO 2016 binary-studio.com

2. Data aggregations

3. Data aggregations ● Metrics ● Bucketing ● Pipeline (experimental)

4. Metrics aggregation ● avg (average value by field) ● cardinality (approximate count of distinct values) ● stats ● geo ● sum ● min ● max ● value_count

5. Metrics aggregation example curl localhost:9200/github/repository/_search?pretty -d '{ "aggs" : { "stats_watchers" : { "stats" : { "field" : "watchers_count" } } } }'

6. Bucket aggregation ● children ● date_range ● date_histogram ● filter(s) ● histogram ● geo ● global ● range ● ip ● terms ● nested

7. Bucket aggregation histogram curl localhost:9200/github/repository/_search?pretty -d '{ "aggs" : { "prices" : { "histogram" : { "field" : "forks_count", "interval" : 1000 } } } }'

8. Bucket aggregation terms curl localhost:9200/github/repository/_search?pretty -d '{ "aggs" : { "language" : { "terms" : { "field" : "language" } } } }'

9. Pipeline aggregation (experimental) ● Avg bucket ● Derivative ● Max bucket ● Min bucket ● Sum bucket ● Stats bucket ● Moving avg bucket ● Percentiles bucket

10. Pipeline aggregation example curl localhost:9200/github/repository/_search?pretty -d '{ "aggs" : { "language_term" : { "terms" : { "field" : "language" } }, "language_avg":{ "stats_bucket": { "buckets_path": "language_term" } } } }'

11. Does it support sql-like JOINS?

12. Nope

13. Relationships handling

14. Relationships handling • Application-side joins • Data denormalization • Nested objects • Parent/child relationships (1:M)

15. Elasticsearch usage

16. Elasticsearch usages ● Enterprise search ● Analytics

17. ELK stack

18. ELK stack

19. ELK stack data flow

20. Client libraries

21. Client libraries ● Java API ● elasticsearch.js ● Elasticsearch.Net and NEST ● Elasticsearch-PHP Checkout others client libraries

22. var elasticsearch = require('elasticsearch'); var client = new elasticsearch.Client({ host: 'localhost:9200' }); client.search({ index: 'github', type: 'repository', body: { query: { term: { language: 'C++' } } } }).then(function (resp) { var hits = resp.hits.hits; }, function (err) { console.trace(err.message); }); JS starter

23. $clientBuilder = ElasticsearchClientBuilder::create(); $clientBuilder->setHosts(['localhost:9200']); $client = $clientBuilder->build(); $params = [ 'index' => 'github', 'type' => 'repository', 'body' => [ 'query' => [ 'term' => [ 'language' => 'C++' ] ] ] ]; $response = $client->search($params); print_r($response); PHP starter

24. That’s all

Academy PRO: Elasticsearch Misc

Recommended

More Related Content

What's hot (19)

Similar to Academy PRO: Elasticsearch Misc (20)

More from Binary Studio (20)

Recently uploaded (20)

Academy PRO: Elasticsearch Misc